Method for Simulating Thoracic 4DCT

ABSTRACT

Four-dimensional (4D) computed tomography (CT) is simulated by first generating a surface mesh from a single thoracic CT scan. Tetrahedralization is applied to the surface mesh to obtain a first volume mesh. Finite element analysis, using boundary constraints and load definitions, is applied to the first volume mesh to obtain a lung deformation according to an Ogden model. Constrained tetrahedralization, using control points, is applied to the lung deformation to obtain a second volume mesh, which is then deformed using mass-spring-damper simulation to produces the 4DCT.

FIELD OF THE INVENTION

The invention relates generally to four-dimensional, computed tomography(4DCT), and more particularly to simulating respiration for radiotherapytreatment planning.

BACKGROUND OF THE INVENTION

The use of four-dimensional computed tomography (4DCT) is a commonpractice in radiation therapy treatment planning for thoracic regions.The information from 4DCT allows a radiation oncologist to plan accuratetreatments for moving tumors, deliver radiation within predeterminedcertain interval in the breathing cycle, and reduce the risk oftreatment-related side effects.

Methods for 4DCT include retrospective slice sorting, and prospectivesinogram selection. For the retrospective slice sorting method, theprojection data are continuously acquired for a time interval longerthan a respiratory cycle. Multiple slices corresponding to differenttimes are reconstructed and sorted into respiratory phase bins usingvarious respiratory signals. For the prospective sinogram selectionmethod, the scanner is triggered by the respiratory signal. Then, theprojection data within, the same phase bin are used to reconstruct CTslices corresponding to that breathing phase.

Both methods are time consuming and considerably increase the radiationdose for the patient. For example, the radiation dose of a conventional4DCT scan is about six times a typical helical CT scan, and 500 times achest X-ray. Moreover, 4DCT acquisition alone cannot determine the tumorposition in-situ. These facts are a major concern in the clinicalapplication of 4DCT, motivating development of advanced 4DCT simulators.

Various methods are known for modeling lung inflation and deflation. Thefirst category of methods discretize the soft tissues and bones intomasses (nodes), and connect the nodes using springs and dampers (edges)based on a mass-spring-damper system, and CT scan values forspline-based mathematical cardiac-torso (MCAT) phantoms, augmentedreality based medical visualization, respiration animation, and tumormotion modeling. A common approach applies affine transformations tocontrol points to simulate respiratory motion. Lungs and body outlineare linked to the surrounding ribs for synchronized expansion andcontraction. Those simple and fast methods only provide approximate lungdeformations when compared with accurate models based on continuummechanics.

The second category of methods use hyperelastic models to describe thenon-linear stress-strain behavior of the lung. To simulate lungdeformation between two breathing phases (T_(i),T_(i+1)), the lung shapeat phase T_(i+1) is used as the contact or constraint surface, anddeform the lung at phase T_(i) based on the predefined mechanicalproperties of lung. A negative pressure load is applied on the lungsurface, and finite element (FE) analysis is used to deform tissues. Thelung expands according to the negative pressure, and slide against thecontact surface to imitate the pleural fluid mechanism. The pressure canbe estimated from the pleural pressure versus lung volume curve, whichmeasured from a pulmonary compliance test.

In addition to lung deformation, the displacements of the rib cage anddiaphragm are also very important to design a realistic 4DCT simulator.Rib cage motion can be a rigid transformation. A finite helical axismethod can be used to simulate the kinematic behavior of the rib cage.The method can be developed into a chest wall model relating the ribsmotion to thorax-outer surface motion for lung simulation.

A simple diaphragm model includes central tendon and peripheral muscularfiber. Then, cranio-caudal (CC) forces are applied to each node of themuscular fiber to simulate the contraction of the diaphragm. AGauchy-Green deformation tensor can model the lung deformation. Organsinside the rib cage can be considered as a convex balloon to estimate aninternal deformation field directly by interpolation of skin markermotions.

SUMMARY OF THE INVENTION

The embodiments of the invention provide a method for simulatingthoracic 4DCT. The method uses a biomechanical motion model that canfaithfully simulate deformation of the lung and nearby organs for anentire respiration cycle. Deformations of other organs can also besimulated.

As an advantage, the method only requires a single CT scan as input.Loads on the rib cage and the diaphragm constrain the deformation of thelung. This distinguishes the present method from conventional continuummechanics based methods. The method can also simulate a passivemass-spring model based deformation of abdominal organs due torespiration.

The lung stress-strain behavior is modeled using a hyperelastic model.The lung deformation problem is solved by finite element (FE) analysis.Diaphragm control points and spine regions are segmented out tocarefully define the boundary conditions and the load definitions, andto improve FE convergence using a mesh optimization procedure.

The remaining CT volume is treated as discretized mass points connectedby springs and dampers. The motion of liver, bones and other organs canbe simulated using finite difference analysis.

This heterogeneous design can leverage the advantages of both continuummechanics and mass-spring-damper system in the way that the lungdeformation is modeled with a very high accuracy, while the deformationof the rest of the CT volume is achieved under practical computationconstraints.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2 are flow diagrams of a method for simulating thoracic 4DCTaccording to embodiments of the invention;

FIG. 3 is a sample 2D axial view for a maximized region covered aGaussian function according to embodiments of the invention;

FIG. 4 is an image of a dense 3D point cloud for lung voxels accordingto embodiments of the invention;

FIG. 5 is an image of the lung after converting the point of FIG. 4cloud into a weight map according to embodiments of the invention;

FIG. 6 is a sample weight map according to embodiments of the invention;

FIG. 7 is a schematic of sample blocks on the lung surface for a weightcalculation procedure accord in to embodiments of the invention;

FIG. 8 is a schematic of masking an inner region of the lung from anouter boundary according to embodiments of the invention;

FIG. 9 is a schematic of a ribbon formed after the masking of accordingto embodiments of the invention;

FIG. 10 is a schematic of an irregular surface mesh according toembodiments of the invention; and

FIG. 11 is a schematic of an optimized surface mesh according toembodiments of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Thoracic 4DCTSimulator Method

FIGS. 1 and 2 show a processing pipeline of a thoracic four-dimensionalcomputed tomography (4DCT) simulator method according to embodiments ofthe invention. The method includes lung deformation and 4DCT scansimulation. The method can be performed in a processor connected tomemory and input/output interfaces as known in the art. It should beunderstood that the invention can also be used to simulate other typesof tissue or organ deformations, and utilize other types of medicalscans known in the art.

Input to the method is a single thoracic CT scan 101. The CT scan issegmented 110 to generate a surface mesh 111 of the lung.Tetrahedralization 120 is applied to the surface mesh to obtain a firstvolume mesh 121.

Finite element (FE) analysis 130, using boundary constraints and loaddefinitions 138, are applied to the first volume mesh to obtain a lungdeformation 131. The lung deformation is according to an Ogden model,wherein the lung is modeled using a hyperelastic function based onnonlinear continuum mechanics, such that the tumor motion trajectoriesinside the lung are accurate.

Continuing with in FIG. 2, constrained tetrahedralization 150, usingcontrol points 149, is applied to the lung deformation 131 to obtain asecond volume mesh 151, which is a refinement of the first volume mesh.The refined mesh coincides with or is partly or entirely outside thefirst volume mesh. In other words the second mesh corresponds to arelined organ volume. That is, the refined organ volume accuratelyrepresent the actual shape of the organ during the simulation.

Then, mass-spring-damper simulation 160 produces the simulated thoracic4DCT 161. The mass-spring-damper model is used to deform the CT volume.Thus, segmentation of rig cage, spines, and other organs, as in theprior art, is unnecessary.

Our simulator can generate any desired number of lung deformationsbetween a maximal exhale phase T_(ex) and the maximal inhale phaseT_(in), which are used to synthesize the corresponding 4DCT phases.

The method can construct 4DCT phases with or without hysteresis of thetissue deformation, which defines how the lung; volume is correlatedwith the inhale-to-exhale motion path and the exhale-to-inhale motionpath. Without hysteresis, the CT scan for the same lung volume statesare identical regardless of whether the lung is in the inhale-to-exhalecycle or the inhale-to-exhale cycle.

For a particular lung volume, our method assembles the corresponding4DCT phases to generate a simulated sequence of CT's. In addition to thelung volume, pleural pressure and chest and abdomen height can also beused to synchronize for real-time patient-specific 4DCT, as describedbelow.

The method steps described herein can be performed in a processorconnected to memory and input/output interfaces as known in the art.

Biomechanical Motion Model for Simulation of Lung Deformation

Boundary Constraints

For simplicity of notation, we use x, y, and z to represent lateral,anterioposterior (AP), and superoinferior (SI) direction, respectively.For the lung deformation, we define three types of boundary constraintson the lung surface based on the anatomy and function of the humanrespiratory system. Because the upper lobes of the lung are constrainedby the ribs, the z displacement of the corresponding region are fixed inour simulator to avoid pure translation of the lung in the z directionwhen simulating the diaphragm contracting on the bottom lobes.Intuitively, the top 5% of the surface vertices in the z direction areselected to represent the upper lobes.

During inspiration, the lung sliding against the rib cage mainly occursin the posterior spine region, while in the anterior region, the lungexpands with the increasing of thoracic cavity and the relative slidingis negligible. Thus, we define the boundary conditions for both thefront and the back parts of the lung surface to simulate the differentsliding actions. Our method fixes the z displacement for all the points,generally 139 in FIG. 1, to simulate the coherent motion of the lungwith the thorax expansion on the axial plane. The selection of thesevertices is implemented by the heuristics that the points are on or neara convex hull of the lung surface, around the lateral sides of themiddle and lower lobes, and have small normal variations, e.g., <20 °.

To simulate the pleural sliding in the spine region, our simulatormethod automatically locates the lung surface vertices near the thoracicvertebrae, and fixes the x and y displacements of these points as thethird boundary constraint. Our goal is to find surface vertices close tothe spine. Therefore, we use a Gaussian curve fitting process to locatethese points instead of adopting a complicated thoracic vertebraesegmentation approach, as in the prior art.

The idea is to fit a set of Gaussian curves, such that the area cut outby each curve is maximized. This provides a global approximation to thespine shape, and the constraint points can be accurately located.

FIG. 3 shows a sample 2D axial view, in which our procedure maximizesthe spine region S covered, by the Gaussian function

${f(x)} = {a\; {^{- \frac{{({x - b})}^{2}}{2c^{2}}}.}}$

We formulate the Gaussian function as a constrained multi-variableminimization problem

$\begin{matrix}{{\min\limits_{a,b,c}{- {\sum\limits_{x = x_{\min}}^{x_{\max}}{f(x)}}}},\; {{{s.t.\mspace{11mu} {f(x)}} - {g(x)}} \leq 0},{\forall{x \in \lbrack {x_{\min},x_{\max}} \rbrack}},} & (1)\end{matrix}$

where the parameters a, b, and c represent a scaling factor, an expectedvalue, and a standard variance of f(x), x_(min) and x_(max) are limitson the lung in the lateral direction, and g(x) is an upper limit forf(x), and is the minimal y value of the lung slice at each x.

In our simulator, this constrained minimization problem is solvedefficiently by a sequential quadratic programming method, specificallyan active-set procedure, which determines a sequential quasi-Newtonapproximation to the Hessian matrix of the Lagrangian multiplier at eachiteration. We extend this 2D procedure to the 3D CT volume by applyingthe procedure slice by slice.

Outliers can occur in the top and bottom of the lung where g(x) is onlypartial constrained for the curve fitting. Our simulator removes theoutliers different than a mean Gaussian curve of the set so that correctfittings of the thoracic vertebrae are retained. The missing curves canbe estimated by linear interpolation of the remaining curves.

Loads Definitions

Because there is no hounding surface during inhalation, we design anextra traction applied on the diaphragm area of the lung besides thenegative intra-pleural surface pressure. The pressure force inflates thelung in all directions during inspiration, while the traction allowsadditional displacement in the z direction to simulate the diaphragmcontraction and pleural sliding. Hence, the lung surface mesh islikewise inflated during the simulation.

The pressure force is defined from the simulator input. Therefore, wefocus on accurately locating the points that are close to the diaphragmfor the definition of the traction. We model this as a graph searchproblem, solve the problem by a modified shortest closed-path procedure.

As shown in FIG. 4, our simulator first determines a dense 3D pointcloud by finding the lung voxels at every (x,y) location with thelargest z value.

As shown in FIG. 5, we convert the point cloud into a weight map basedon the local geometry information, and locate the diaphragm points usingour modified shortest closed-path procedure. The left and right, lowerlobes are treated separately.

Weight Map

We consider the 3D point cloud as an 2D image with intensity value fromthe z value of the corresponding point, and run a local line directiondiscrepancy (LDD) computation on this image to generate a weight map W.Thus, our weight map computation is a special type of image filtering.

As shown in FIG. 6 for each line d_(i)(x,y) of a block centering at(x,y), we construct two sub-lines d_(i) ¹(x,y) and d_(i) ²(x,y) from(p_(i) ³, p_(i) ², p_(i) ¹) and (p_(i) ³, p_(i) ⁴, p_(i) ⁵)respectively, (i=1, . . . , 4), and determine the LDD as the minimumintersection angle of the four sub-line pairs.

Alternatively, we can determine the maximum of the cosine value of theseangles to represent the weight, which can be determined by a dot productas

$\begin{matrix}{{{W(p)} = {\max\limits_{{i = 1},\ldots \mspace{11mu},4}\{ \frac{d_{i}^{1} \cdot d_{i}^{2}}{{d_{i}^{1}} \cdot {d_{i}^{2}}} \}}},} & (2)\end{matrix}$

where p represents pixel position (x,y), and the block size is 5×5, forsimplicity. Intuitively, regions with high curvature have high positiveLDD values, for example, d₁ of B₃ in FIG. 7, while flat regions have lownegative LDD values, for example. B₁ and B₂.

Diaphragm Point Segmentation

Notice that all outliers are located at the boundary of the weight map.Therefore, we formulate the diaphragm point segmentation as a shortestclosed-path (SCP) problem, which finds a optimal cut along the boundarythat separates the diaphragm points from the outliers. To construct thegraph for SCP, we select four neighborhood connection and set the edgeweight E_(pq) as W(q). Therefore, E_(pq) and E_(qp) can have differentweights, instead of using the entire weight map to construct the graph.

As shown in FIG. 8, we mask out the inner region with morphologicaloperations, and limit an optimal cut between the inner boundary ∂Ω₂ andthe outer boundary ∂Ω₁. If we use the conventional SCP procedure, thensome interior regions can be cut out to favor the lowest cost. Instead,we first sample the outer boundary ∂Ω₁ every ten points, and locate thecorresponding points, in terms of Euclidean distance, on the interboundary ∂Ω₂.

For the remaining points on the outer boundary ∂Ω₁, we determine theirmatches on the inner boundary ∂Ω₂ by linear interpolation of theprevious matches, such that there are no crossing matches (lines) andcorrect ordering is maintained. In this way as shown in FIG. 9, we can“unbend” the ring region between ∂Ω₁ and ∂Ω₂ into a flat ribbon byalignment in order, and set the length of the ribbon as the length ofthe outer boundary ∂Ω₁, and the width as the shortest distance betweenouter boundary ∂Ω₁ and the inner boundary ∂Ω₂. Then, we construct a newadjacency matrix graph from the ribbon for the SCP procedure. Thisyields an accurate model for the diaphragm for the traction definition.

Finite Element Simulation

The final step for biomechanical simulation of lung deformationaccording to embodiments of the invention is to define the materialproperty of the lung and apply the FE analysis. We assume the lungtissue is homogeneous, isotropic, and use a first-order Ogden model. Thewell-known Ogden model is a hyperelastic material model used to describethe non-linear stress-strain behavior of complex materials such asbiological tissue. The Ogden model, like other hyperelastic materialmodels, assumes that the material behavior can be described by means ofa strain energy density function, from which the stress-strainrelationships can be derived. These materials can generally beconsidered to be isotropic, incompressible and strain rate independent.Other models that can be used include a Mooney-Rivlin model, a linearelastic model, a linear visco-elastic model, or combinations thereof.

The Ogden odd describes a non-linear strain energy density function as

$\begin{matrix}{{{W( {\lambda_{1},\lambda_{2},\lambda_{3},J} )} = {{\frac{\mu_{1}}{\alpha_{1}}( {\lambda_{1}^{\alpha \; 1} + \lambda_{2}^{\alpha_{1}} + \lambda_{3}^{\alpha_{1}} - 3} )} + {\frac{K}{2}( {\ln \; J} )^{2}}}},} & (3)\end{matrix}$

where λ_(1,2,3) are the deviatoric principal stretches, μ₁ and α₁ arematerial constants, J is the Jacobian matrix for the lung deformation,and K is a bulk modulus selected sufficiently high to satisfynear-incompressibility. For the parameters of the Ogden model, we usee.g., μ₁=0.0329, and α₁=6.82.

We combine all the information (meshes, loads, and boundaries) definedabove into a single script file and directly run the FE analysis tosimulate the lung deformation using FEBio developed at the University ofUtah Musculoskeletal Research Laboratories. FEBio is an open sourcenonlinear finite element solver specifically designed for biomechanicalapplications. One important reason that we choose FEBio is that it isspecifically designed for biomechanical applications, and offersconstitutive models and boundary conditions that are relevant to themodeling of soft tissue deformation. The wide range of boundaryinteractions supported in FEBio makes the modeling of pleural slidingvery straightforward.

4DCT Scan Simulation

The deformation of the rest of the CT volume is very complicated if wewere to use a prior art continuum mechanics based method, which requiressegmenting out all the soft tissues, organs, bones, and otherstructures, and manually defining different material properties andtheir corresponding boundary constraints. Instead, we treat the rest ofthe CT volume as a discrete structure of elements and model the CTvolume deformation using a mass-spring simulation system.

System Element Definitions

We construct the mass-spring system by sampling control points (masses)in the CT volume excluding the lung region. Then, we connect thesepoints using a constrained Delaunay tetrahedralization procedure to formthe links (springs), as a cutting plane. To define mass values for eachcontrol points, we first determine their physical densities from the CTimage values, i.e., Hounsfield units (HU). The conversion from densityto mass is straightforward. The HU versus electron/physical densitiescurve for human organs and tissues is available for specific CT devices,e.g.,

$\begin{matrix}{{\rho (x)} = \{ \begin{matrix}{{1.1\; e^{- 3}}} & {{{{{if}\mspace{14mu} {h(x)}} < {- 13}},}} \\{{{6.0\; {e^{- 4} \cdot {h(x)}}} + 1.081}} & {{{otherwise},}}\end{matrix} } & (4)\end{matrix}$

where ρ(x) represents the physical density g/cm³ at position x, and h(x)HU value.

The guideline to define spring constants for the mass-spring system isto assign large values to relatively dense bones, small values to softtissues, lung, blood, air, etc. based on their HU values. Consideringthe HU value for bones ranging from +700 to 3000 (dense bone), wenonlinearly map the HU values [700, 3000] to spring constants [0.8 1]based on the reciprocal of an exponential function, while the springconstants of other materials are mapped to [0.001 0.8]. Other heuristicmappings are also possible and have little impact on the systemperformance.

Dynamics and Forces

We deform the rest of the CT volume based on the lung deformation ateach breathing phase. Therefore, our system is an unforced version of aconventional mass-spring-damper system, that is, the external forceF_(ext)=0. The input is the prescribed displacements of the lung surfacevertices, which generates the tensions for the internal springs byHooke's Law after each lung deformation. We also add damping force F_(d)to reduce the amplitude of system oscillations. Thus, from Newton'ssecond law, the total force of the system F_(tot) is equal to theinternal force F_(int), and defined as

$\begin{matrix}{{F_{tot} = {F_{int} = {{F_{s} + F_{d}} = {m \cdot \frac{^{2}y}{t^{2}}}}}},} & (5)\end{matrix}$

where F_(s) is the spring tension defined as F_(s)=−k·y, F_(d)=−·dy/dt,k is the n×1 spring constant vector, y is the displacement vector of then control points, m is the mass vector corresponding to the controlpoints, and operator denotes the inner product of two vectors. We definethe system as a critically damped system, therefore, the damping factorR=2√{square root over (m·k)}. Thus, Eqn. 5 can be derived as,

m·ÿ+R·{dot over (y)}+k·y=0,  (6)

where the dots above the variables indicate the respective, derivatives.

We solve this dynamic system using a finite difference method. Betweeneach iteration, we fix the motion of mass points covered, by the spineregion to simulate the stationary spine movement. After we have thedeformation of the control points, we can determine the deformations ofall voxels by trilinear interpolation. Other interpolation method, suchas, tricubic interpolation, can also be used to obtain smoother andcontinuous differentiable volumes.

Volume Mesh Generation

To generate the lung volume mesh for the FE analysis of the lungdeformation, we use 3D tetrahedralization of the surface mesh. We canhighlight image regions where user labeling could improve thesegmentation results. However, because the tetrahedralization is forgeneral purpose segmentation and not specialized for lung segmentations,additional mesh editing may be required to fix small missing pieces andholes. The number of surface vertices can be reduced to ˜4000 usingquadric edge collapse decimation for simplification. After the surfacemesh is given, the lung simulation can run automatically.

FIG. 10 shows a portion of a surface mesh. One common problem of thesurface mesh generated from segmentation tools is that the mesh ishighly irregular, and contains many thin triangles and over-sampledregions. This can lead to badly shaped elements during the 3D Delaunaytetrahedralization process and delay convergence of biomechanicalsimulations. To optimize the surface mesh, our simulator converts thesurface mesh into a solid volume, and then determines a remeshed surfaceusing a marching cube procedure over the volume. This mesh optimizationcan also fix many other problems with the segmentation results, forinstance, self-intersecting faces and non-manifold edges and vertices.

FIG. 11 shows that the optimized surface contains well-shaped trianglesand uniformly sampled vertices, which constitute a valid basis for 3DDelaunay tetrahedralization.

Our biomechanical motion simulation method for lung deformation is veryaccurate considering a very low spatial resolution of the CT data(0.97×0.97×2.5 mm), especially in z direction. The method works well inthe lower posterior region of the lung where nodal displacement ismostly prominent. This indicates that our lung deformation simulator canprovide valuable location-specific tumor motion in to significantlyreduce the margins between clinical target volume (CTV) and planningtarget volume (PTV) to potentially improve survival rates forlung-cancer patients.

4DCT Scan Simulation

To generate simulated 4DCT scans, we treat the lung deformation as theequivalent force that drives the deformation of the CT volume because wehave already considered all the influencing factors, for instance, themovement of body outline and diaphragm, into the biomechanicalsimulation of lung deformation. Therefore, or every lung deformation, wecan determine its corresponding CT volume deformation through ournonlinear mass-spring-damper system.

During, our simulation, the abdominal organs are squeezed down duringrib cage expansion, while the spines are fixed at the same time. Thisdemonstrates that our simulator is capable of providing realisticdeformation and displacement of liver, bones, and other soft tissuesbesides the accurate respiratory motion of lung.

Tumor Embedding

In our simulator, tumors can be embedded into the lung volume meshthrough determining barycentric coordinates of their vertices to nearbylung tetrahedra. During X-ray images integration, we can also synthesizethe ground truth 3D tumor contours. By putting different types of tumorat different positions of the lung, our simulator is able to provideunbiased ground truth for validating the accuracy of different, tumortracking algorithms. Compared with X-ray videos with metallic markers,our X-ray images remove the side effects of high Contrast regionsintroduced by markers and can provide realistic images for trackingprocedures to extract accurate region features. Our simulator alsoeliminates the need for manually drawing ground truth tumor contours,which can contain large inter- and intra-physician discrepancies.

Our simulator can be extended to simulate real-time patient-specific4DCT scans for particular synchronized pleural pressure and chest andabdomen height using only the single CT scan as input. In this case, theFE analysis of the lung deformation converges very quickly because thereis only a small difference of the lung shape between two time steps. Thepleural pressure can be estimated from the lung volume readings throughthe pressure-volume curve, and yields the precise negative pressure fordefining the deformation force.

The synchronized chest and abdomen height yield the patient body outlineduring respiration, and define the boundary constraints for both lungdeformation and 4DCT scan simulation.

The invention provides biomechanical motion model based thoracic 4DCTsimulation method for patient lung deformation induced by respirationusing only one CT scan as input. The method models lung stress-strainbehavior using a hyperelastic Ogden model, and treat the rest of the CTvolume as discretized mass points connected by springs and dampers.

This heterogeneous design leverages the advantages of both continuummechanics and mass-spring-damper system in the way that the lungdeformation is determined with high accuracy, while the deformation ofthe rest of the CT volume is achieved under practical computationalconstraints. Extensive analysis and comparisons with the manuallylabeled dataset demonstrate that our lung deformation results are veryaccurate.

Although the invention has been described by way of examples ofpreferred embodiments, it is to be understood that various otheradaptations and modifications may be made within the spirit and scope ofthe invention. Therefore, it is the object of the appended claims tocover all such variations and modifications as come within the truespirit and scope of the invention.

We claim:
 1. A method for simulating four-dimensional (4D) computedtomography (CT), comprising the steps of: generating a first set ofvolumes from a single thoracic CT scan; deforming the first set ofvolumes according to a biomechanical motion model to obtain a set ofdeformed volumes; constructing a second set of volumes using the set ofdeformed volumes; and deforming the second set of volumes according to amass-spring model using the CT scan to produce the 4DCT, wherein thesteps are performed in a processor.
 2. The method of claim 1, whereinthe first set of volumes represent a lung, a heart, a diaphragm, a ribcage, and other segmented organs and tissues in the CT scan.
 3. Themethod of claim 1, wherein the first set of volumes is represented by asurface mesh of vertices.
 4. The method of claim 2, further comprising:generating a lung surface mesh of vertices as the first set of volumes;applying tetrahedralization to the lung surface mesh; generatingboundary constraints and load definitions on the lung surface mesh; and,applying finite element (FE) analysis to the lung volume mesh to obtaina lung deformation according to the biomechanical motion model of thelung.
 5. The method of claim 4, wherein the biomechanical motion modelof the lung is an Ogden model, a Mooney-Rivlin model, a linear elasticmodel, a linear visco-elastic model, or combinations thereof.
 6. Themethod of claim 4, wherein the boundary constraints consider a lateraldisplacement of the vertices on upper lobes of the lung is fixed, thelateral displacement of the vertices on and near a convex, hull of thelung surface mesh and around lateral sides of middle and lower lobes ofthe lung is fixed, and anterioposterior, and superoinferiordisplacements of the vertices in a vicinity of a thoracic vertebrae arefixed.
 7. The method of claim 4, wherein a pressure force inflates thelung surface mesh and a traction force applies to displace the diaphragmalong the lateral direction to simulate contraction of pleural slidingof the diaphragm during an inspiration phase of the lung.
 8. The methodof claim 7, further comprising: defining a weight map; masking an innerregion of the lung by morphological operations to obtain an innerboundary and an outer boundary of the lung; sampling the outer boundaryand locating corresponding inner points; interpolating remaining points;construction a ribbon belt from the points; building an adjacency graphfrom the ribbon belt; and determining a shortest path for the adjacencygraph to obtain an area representing the diaphragm.
 9. The method ofclaim 4, wherein the boundary conditions simulate pleural sliding in aspine region using a set of Gaussian curves.
 10. The method of claim 9,further comprising: generating a set of two-dimensional axial slicesalong a lateral direction from the lung volume mesh; fitting theGaussian curves to an area under the lung corresponding to the spineregion at each slice by applying a sequential quadratic programmingusing an active-set that determines a quasi-Newton approximation to aHessian matrix of a Lagrangian multiplier at each iteration; defining aconstrained minimization problem using the Gaussian curves; removingoutliers different to mean Gaussian curve of the set of Gaussian curves;and estimating missing curves by linear interpolation.
 11. The method ofclaim 4, wherein the lung is segmented using the single thoracic CT scanto generate the lung surface mesh.
 12. The method of claim 9, whereinthe tetrahedralization considers loads on the rib cage and thediaphragm.
 13. The method of claim 1, further comprising: applyingconstrained tetrahedralization, using control points, to the set ofdeformed volumes to obtain the second volume mesh; and deforming thesecond volume mesh using the mass-spring model to produces the 4DCT. 14.The method of claim 13, wherein the second volume covers the set ofdeformed volumes outside the set of first volume meshes.
 15. The methodof claim 13, wherein the mass-spring model treats the set of secondvolumes as a discrete structure of internal springs where parameters ofmass, damping forces, and constants, are assigned to vertices in thesecond set of volumes by converting image values of the CT scan, and thedisplacement between the first volume mesh and deformed mesh generatestension parameters for the internal springs by Hooke's Law after eachlung deformation.
 16. The method of claim 1, wherein the simulation isbetween a maximal exhale phase and the maximal inhale phase during, thelung deformation with or without hysteresis of tissue deformation. 17.The method of claim 1, further comprising: embedding a tumor into theset of first volumes by determining barycentric coordinates of verticesof the tumor to nearby lung tetrahedra.
 18. A method for simulatingcomputed tomography (CT), comprising the steps of: generating a surfacemesh from a single thoracic CT scan; applying tetrahedralization to thesurface mesh to obtain a first volume mesh; applying finite element (FE)analysis to the first volume mesh, using boundary constraints and loaddefinitions, to obtain a lung deformation according to an Ogden model;applying constrained tetrahedralization, using control points, to thelung deformation to obtain a second volume mesh; and deforming thesecond volume mesh using mass-spring-damper simulation to produces theCT, wherein the steps are performed in a processor.